Remote Volunteer Retention Metrics
This dataset provides comprehensive metrics on remote volunteer participation, engagement, and retention for nonprofit organizations, including activity types, feedback, and program manager assignments. It enables program managers to identify engagement gaps, monitor retention trends, and optimize volunteer experiences in digital-first environments. The data structure supports actionable insights for improving distributed volunteer programs.
Sample rows
preview · 8 of 90 rows · all 16 columns| volunteer_idstring | retention_statusstring | feedback_scorefloat | countrystring | organization_idstring | volunteer_namestring | emailstring | join_datedate | last_active_datedate | total_hours_volunteeredfloat | number_of_activitiesinteger | activity_typesstring | engagement_scorefloat | last_feedback_datedate | program_manager_idstring | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| VOL001 | active | 4.8 | United States | ORG1001 | Sophia Nguyen | sophia.nguyen@ | 2023-02-15 | 2024-05-30 | 172.5 | 14 | mentorship,admin support,community outreach | 93.2 | 2024-05-28 | PM304 | Consistent participation in multiple programs; excellent peer feedback. |
| VOL002 | active | 4.2 | India | ORG1002 | Rajiv Patel | rajiv.patel@ | 2022-09-08 | 2024-05-10 | 85 | 8 | fundraising,mentorship | 76.4 | 2024-05-12 | PM210 | Strong in mentorship, moderate fundraising engagement. |
| VOL003 | at-risk | 3.8 | Sweden | ORG1003 | Lina Svensson | lina.svensson@ | 2023-03-22 | 2024-03-30 | 25.5 | 3 | community outreach | 58.3 | 2024-04-05 | PM134 | Recent drop in activity; at-risk flagged. |
| VOL004 | inactive | 2.6 | Canada | ORG1001 | Michael Johnson | mjohnson@ | 2022-11-05 | 2023-12-10 | 11.5 | 2 | admin support | 31.2 | 2023-12-09 | PM304 | Inactive since last winter; outreach recommended. |
| VOL005 | active | 4.6 | Egypt | ORG1004 | Amira Khalid | amira.khalid@ | 2023-04-21 | 2024-05-25 | 149 | 12 | fundraising,mentorship,community outreach | 89 | 2024-05-26 | PM430 | Key contributor; leads multiple activities. |
| VOL006 | at-risk | 3.5 | South Korea | ORG1002 | David Kim | david.kim@ | 2023-01-10 | 2024-04-18 | 67 | 6 | fundraising,admin support | 61.5 | 2024-04-15 | PM210 | Recent decrease in engagement. |
| VOL007 | alumni | 4.5 | United Kingdom | ORG1005 | Sarah O'Connor | sarah.oconnor@ | 2022-07-19 | 2024-01-12 | 105 | 9 | community outreach,mentorship | 74.3 | 2024-01-10 | PM512 | Transitioned to alumni status after 2 years of service. |
| VOL008 | inactive | 3.1 | Spain | ORG1003 | Carlos Martinez | carlos.martinez@ | 2023-08-28 | 2024-02-15 | 38 | 4 | admin support,community outreach | 49.7 | 2024-02-12 | PM134 | Inactive since February. |
| VOL009 | active | 4.4 | Pakistan | ORG1006 | Fatima Hassan | fatima.hassan@ | 2023-06-14 | 2024-05-20 | 122 | 10 | mentorship,fundraising | 81.7 | 2024-05-21 | PM278 | Regular mentor and fundraiser. |
| VOL010 | active | 4.7 | Australia | ORG1005 | James Lee | james.lee@ | 2022-10-23 | 2024-05-15 | 133.5 | 11 | community outreach,admin support | 86.1 | 2024-05-14 | PM512 | High engagement across outreach programs. |
| VOL011 | at-risk | 3.9 | Italy | ORG1001 | Maria Rossi | maria.rossi@ | 2023-03-01 | 2024-03-04 | 60 | 6 | fundraising,mentorship | 69.3 | 2024-03-02 | PM304 | Decreased activity; at-risk warning sent. |
| VOL012 | active | 4 | Singapore | ORG1004 | Emily Chen | emily.chen@ | 2023-12-06 | 2024-05-18 | 56 | 5 | admin support | 63 | 2024-05-17 | PM430 | Steady engagement; positive feedback. |
| VOL013 | inactive | 2.9 | Egypt | ORG1006 | Ahmed El-Sayed | ahmed.elsayed@ | 2022-09-14 | 2023-11-20 | 19 | 2 | fundraising | 34.5 | 2023-11-18 | PM278 | Not engaged past last autumn. |
| VOL014 | active | 4.3 | United Kingdom | ORG1002 | Jessica Clark | jessica.clark@ | 2023-05-05 | 2024-05-25 | 102.5 | 9 | community outreach,mentorship | 80.9 | 2024-05-22 | PM210 | Consistent engagement in outreach. |
| VOL015 | alumni | 4.1 | Ireland | ORG1005 | Oliver Evans | oliver.evans@ | 2023-02-04 | 2024-01-09 | 49 | 5 | admin support | 55.2 | 2024-01-07 | PM512 | Completed volunteer term. |
| VOL016 | active | 4.9 | Germany | ORG1003 | Anna Müller | anna.muller@ | 2023-07-17 | 2024-05-24 | 132 | 11 | mentorship,community outreach | 87.4 | 2024-05-23 | PM134 | Outstanding mentor; excellent feedback. |
| VOL017 | at-risk | 3.6 | Mexico | ORG1001 | Juan Rodriguez | juan.rodriguez@ | 2023-09-02 | 2024-04-10 | 78 | 7 | fundraising,admin support | 70.2 | 2024-04-09 | PM304 | Needs more diverse activity participation. |
| VOL018 | active | 4.5 | France | ORG1004 | Chloe Martin | chloe.martin@ | 2023-08-10 | 2024-05-29 | 95.5 | 8 | mentorship,community outreach | 77.2 | 2024-05-28 | PM430 | Strong contributor in mentorship programs. |
| VOL019 | inactive | 2.8 | United Arab Emirates | ORG1006 | Yasmin Ali | yasmin.ali@ | 2022-12-12 | 2024-03-11 | 53 | 4 | fundraising | 46.8 | 2024-03-09 | PM278 | Inactive since March. |
| VOL020 | active | 4.2 | Brazil | ORG1002 | Lucas Silva | lucas.silva@ | 2023-11-20 | 2024-05-19 | 59.5 | 5 | admin support,community outreach | 65.8 | 2024-05-17 | PM210 | Steady engagement; positive team feedback. |
What the 90 rows show
from the 90-row sampleInactive (retention status) stands out: mean feedback_
- 4.1median feedback_
score - 16program managers
- 17organizations
- 26activity typeses
- 43.5median total_
hours_ volunteered - 5median number_
of_ activities
Median 4.1, from 2.0 to 5.0.
- string 9
- integer 1
- float 3
- date 3
Columns
16 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 9 columns | |||
volunteer_id | string | Unique identifier for each remote volunteerunique | VOL001 |
organization_id | string | Unique identifier for the nonprofit organization | ORG1001 |
volunteer_name | string | Full name of the volunteer | Sophia Nguyen |
email | string | Email address of the volunteerunique | emily.chen@ |
country | string | Country of residence of the volunteer | United States |
activity_types | string | Comma-separated list of activity types the volunteer participated in (e.g., mentorship, fundraising, admin support)optional | fundraising,mentorship |
retention_status | string | Current retention status (e.g., active, at-risk, inactive, alumni)active · at-risk · inactive · alumni | active |
program_manager_id | string | Unique identifier for the program manager responsible for this volunteeroptional | PM304 |
notes | string | Additional notes or comments about the volunteer’s experience or retention riskoptional | Inactive since February. |
| Numbers 4 columns | |||
total_hours_volunteered | float | Cumulative hours volunteered by the individual0 or more | 172.5 |
number_of_activities | integer | Total number of distinct volunteer activities participated in0 or more | 14 |
engagement_score | float | Calculated engagement score (e.g., 0-100) based on participation frequency and activity diversity0 to 100 · optional | 93.2 |
feedback_score | float | Average feedback rating from the volunteer (e.g., 1-5 scale)1 to 5 · optional | 4.8 |
| Dates and times 3 columns | |||
join_date | date | Date when the volunteer joined the organization | 2023-02-15 |
last_active_date | date | Most recent date the volunteer participated in any activityoptional | 2024-05-30 |
last_feedback_date | date | Date of the most recent feedback submitted by the volunteeroptional | 2024-05-28 |
Use it for
A human resources dashboard
Feedback_
score by retention_ status and a breakdown of country. Excel, Power BI or Tableau. Why do the 13 inactive rows have a mean feedback_
score of 2.6? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Volunteers90VOL0014.8activeVOL0024.2activeVOL0033.8at-risk
A software demo
Believable volunteers with organization_
id, volunteer_ name and email to fill a screen in front of a buyer.
This dataset90 rows16 columns
Yours1,000 rows16 columnscountry: UK only
blueprint · remote-volunteer-retention-metrics
Behind this dataset
Same schema. As many rows as you need.
These 90 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each row represents one registered volunteer in a specific program
- Engagement is measured by digital check-ins, training completions, and task submissions
- Retention status is tracked monthly and flagged if inactive for 60+ days
- Demographics include age group, region, and device used for participation
- Volunteers with negative feedback or unresolved support requests are highlighted
Open the blueprint in Data Factory
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- remote-volunteer-retention-metrics